code: def test_deploy_ray(sagemaker_session, ray_tensorflow_version):
      rl = _rl_estimator(
          sagemaker_session,
          RLToolkit.RAY,
          ray_tensorflow_version,
          RLFramework.TENSORFLOW,
          instance_type="ml.g2.2xlarge",
      )
      rl.fit()
      with pytest.raises(NotImplementedError) as e:
          rl.deploy(1, GPU)
      assert (
          "deployment of Ray models is not currently available"
          in str(e.value)
      )
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import pytest
  from mock import MagicMock, Mock, patch
  from sagemaker.mxnet import MXNetModel, MXNetPredictor
  from sagemaker.rl import RLEstimator, RLFramework, RLToolkit, TOOLKIT_FRAMEWORK_VERSION_MAP
  from sagemaker.session_settings import SessionSettings
  from sagemaker.tensorflow import TensorFlowModel, TensorFlowPredictor
label_desc: Use image node_id 12 for calling a global function with example usage: test_deploy_ray(sagemaker_session, ray_tensorflow_version) without return types